# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os

import torch
from cosyvoice.cli.frontend import CosyVoiceFrontEnd
from cosyvoice.cli.model import CosyVoiceModel
from hyperpyyaml import load_hyperpyyaml
from modelscope import snapshot_download


class CosyVoice:

    def __init__(self, model_dir):
        instruct = True if "-Instruct" in model_dir else False
        self.model_dir = model_dir
        if not os.path.exists(model_dir):
            model_dir = snapshot_download(model_dir)
        with open("{}/cosyvoice.yaml".format(model_dir), "r") as f:
            configs = load_hyperpyyaml(f)
        self.frontend = CosyVoiceFrontEnd(
            configs["get_tokenizer"],
            configs["feat_extractor"],
            "{}/campplus.onnx".format(model_dir),
            "{}/speech_tokenizer_v1.onnx".format(model_dir),
            "{}/spk2info.pt".format(model_dir),
            instruct,
            configs["allowed_special"],
        )
        self.model = CosyVoiceModel(configs["llm"], configs["flow"], configs["hift"])
        self.model.load(
            "{}/llm.pt".format(model_dir),
            "{}/flow.pt".format(model_dir),
            "{}/hift.pt".format(model_dir),
        )
        del configs

    def list_avaliable_spks(self):
        spks = list(self.frontend.spk2info.keys())
        return spks

    def inference_sft(self, tts_text, spk_id):
        tts_speeches = []
        for i in self.frontend.text_normalize(tts_text, split=True):
            model_input = self.frontend.frontend_sft(i, spk_id)
            model_output = self.model.inference(**model_input)
            tts_speeches.append(model_output["tts_speech"])
        return {"tts_speech": torch.concat(tts_speeches, dim=1)}

    def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k):
        prompt_text = self.frontend.text_normalize(prompt_text, split=False)
        tts_speeches = []
        for i in self.frontend.text_normalize(tts_text, split=True):
            model_input = self.frontend.frontend_zero_shot(
                i, prompt_text, prompt_speech_16k
            )
            model_output = self.model.inference(**model_input)
            tts_speeches.append(model_output["tts_speech"])
        return {"tts_speech": torch.concat(tts_speeches, dim=1)}

    def inference_cross_lingual(self, tts_text, prompt_speech_16k):
        if self.frontend.instruct is True:
            raise ValueError(
                "{} do not support cross_lingual inference".format(self.model_dir)
            )
        tts_speeches = []
        for i in self.frontend.text_normalize(tts_text, split=True):
            model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k)
            model_output = self.model.inference(**model_input)
            tts_speeches.append(model_output["tts_speech"])
        return {"tts_speech": torch.concat(tts_speeches, dim=1)}

    def inference_instruct(self, tts_text, spk_id, instruct_text):
        if self.frontend.instruct is False:
            raise ValueError(
                "{} do not support instruct inference".format(self.model_dir)
            )
        instruct_text = self.frontend.text_normalize(instruct_text, split=False)
        tts_speeches = []
        for i in self.frontend.text_normalize(tts_text, split=True):
            model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
            model_output = self.model.inference(**model_input)
            tts_speeches.append(model_output["tts_speech"])
        return {"tts_speech": torch.concat(tts_speeches, dim=1)}
